很多企业的最高管理者都非常热衷于宣传自己的公司已经拥有了各种技术，并且从理论上讲，这些技术可以让他们的公司在不久的将来推出自动驾驶汽车。但是，坦白说，这其中至少有一个领域需要的不仅仅是深层次的技术知识，这里面还需要“表面”上的技术支持。

只有对路面状况具有全面了解和足够重视，自动驾驶汽车的智能系统才能识别道路上可能对汽车行驶造成影响的各种因素，小到某条小路上的坑洼起伏，大至高速公路上的裂缝，从而确保整个行驶过程不受到任何影响，从而避免可能出现的灾难性后果。

rFpro模拟器软件公司技术主管Chris Hoyle表示，要实现这个目标，需要开展全面综合的测试项目，其中需要动用由OEM提供的覆盖所有市场路面的先进扫描技术。为法拉利F1赛车操作提供专业技术（详见http://articles.sae.org/13603/）的rFpro公司，正致力于针对这一难题研究解决方案。而它的独门秘籍就是将精确度与全新的省时系统结合起来。

 “自动驾驶测试项目的全部需求才刚开始为人所知，” Hoyle表示。“但实现这些需求的专业技术现在就能研发，而且这些技术带来的信息对提高当今汽车的安全性能同样重要。”

Hoyle解释道，越来越多的新车都是为了满足国际市场的需求而设计的，因此最好从一开始便选择最佳的设计，但这一目标对目前的汽车制造商而言非常困难。若在研发后期才根据不同市场的偏好和特点对车辆进行修改，那么付出的代价一定是高昂的。因此所有制造商都尽早开始测试，以避免这些问题。

在使用全球性研发平台的过程中，各个地区的路面特征各不相同，这是个让人头疼的问题，这是因为只有在原型车生产出来之后，才能进行有效的测试。Hoyle 表示，“驾驶员在环（DIL，Driver In the Loop）模拟器可以让一名人类驾驶员早在实体的原型车制作出来之前，便在虚拟环境中体验汽车的行驶状况；然而该系统的有效性需要获得路面细节信息的支持，而这些信息直到今天为止都没有人可以提供。”

但Hoyle表示，rFpro公司已经开发出了一套解决方案：“rFpro现在能使用全新的路面扫描技术来建立数字路面模型，而且建模的精确度和速度都达到了前所未有的水平。我们可以捕捉信息，并再现各种路面间的区别，比如一段因霜冻而受损的底特律高速公路、平坦的德国干线道路、瑞士阿尔卑斯的山路或其他各种路面。”

这一技术进步使得汽车制造商无论身处世界何处，都能在逼真的虚拟环境中评估汽车底盘对任何道路类型的反应。 “而且他们不必离开办公室就能做到这一点” Hoyle说。

rFpro在数字路面建模技术上取得的突破开始于一个创举。它摈弃了传统的单脉冲激光LIDAR“渡越时间”扫描技术，代之以逐步发出一连串分离激光信号的全新扫描技术。传统技术要求每个信号返回之后才能发出下一个信号，而全新的受控逐步发射技术允许信号重叠，这提高了数据捕捉的速度、数量和质量。

 “新技术可提供相当于传统技术50倍精度的信息细节，准确度也显著提高；它的扫描速度也更快，因此扫描仪可以随着正常车速运作，而非传统的缓慢爬行，” Hoyle表示。“这意味着不仅可以扫描更长的路段，而且在白天也可以工作，且不会干扰到其他道路使用者。”

为了覆盖全球的道路类型，rFpro与各个地区的扫描技术公司开展了合作。这些公司都将最新的逐步扫描技术加入到他们在rFpro的道路勘测项目中所使用的系统。

这项新技术有效缩短了研发时间，从而大大降低了研发成本。

新技术的精确程度可以在这些数字中体现：“采样”每隔5毫米（0.2英寸）可进行一次路面“采样”，精度可达1毫米（0.04英寸）以内。

Hoyle表示，越来越多的汽车制造商希望rFpro捕捉数据，并再现他们最喜欢的测试道路条件：“2012年我们为100公里以上的路面建立了数字模型，2014年为1000公里以上，而今年我们打算建造3000公里左右的模型。”

Hoyle还说，信息量的增加让rFpro转向云端处理和存储技术。云技术不仅拥有无限的成长空间，而且成百上千的CPU内核可以同时进行数据处理，这进一步缩短了时间。“过去制造商使用的模拟器都改编自航空航天业的模拟器，但是为了充分发挥数字建模价值，必须专门为汽车行业开发最先进的模拟器。”

从今年起，rFpro与几家大型OEM展开合作，共同开发自动驾驶汽车的高级驾驶辅助系统。尽管Hoyle不愿推测在2020年前是否有可能实现自动驾驶的商业化生产，但一些公司已将这一年设为其开发目标达成的时间。

汽车行业的最新进展（例如共享平台和自动驾驶汽车）使得虚拟测试变得越来越重要。

Hoyle表示，rFpro的TerrainServer软件目前已获得北美、亚洲和欧洲众多OEM客户的青睐，而大多数F1赛车和NASCAR团队也已加入其中。只有将数字路径（道路）表面模型与诸如轮胎接触面的其他方面相结合，加上信息实时处理技术的开发，才能实现精准的汽车动态模拟。这种能力的实时提供，对DIL测试至关重要。

Road-surface modeling aims to support autonomous driving

Some chief executives are enthusiastically fond of stating that their companies already possess much of the technology that would, in theory, allow them to introduce autonomous vehicles in the very near future. However, there is at least one area that is likely to require not just in-depth knowledge but, quite literally, superficial knowledge.

It is to acquire a total understanding and appreciation of road surfaces that would allow an autonomously operated vehicle’s intelligent systems to recognize and appreciate the potential effects on a vehicle of anything from mild camber changes and potholes on a minor road, to cracks on a freeway, and to ensure that none would cause a possibly catastrophic effect that could endanger a whole high-speed convoy.

To achieve this will involve very comprehensive test programs incorporating the use of advanced surface scanning technology of roads in all markets served by an OEM, said Chris Hoyle, Technical Director of simulator software specialist rFpro. The company, which has technology links with Ferrari's F1 operation (seehttp://articles.sae.org/13603/), is majoring on the creation of solutions to the challenge. And it is doing so by combining accuracy with new time saving systems.

“The needs of autonomous driving test programs are only now starting to be fully understood,” said Hoyle. “But meeting those needs can be achieved via expertise that can be gained now, providing equally significant information to make current vehicles safer.”

Hoyle explains that as increasing numbers of new vehicles are designed to suit global markets, making the optimum design choices at the outset has become essential but is now far more challenging for vehicle manufacturers. Revisions to a vehicle to suit individual market tastes and conditions late in a development program are so costly that manufacturers aim to avoid such issues by testing as early as possible.

Pronounced regional differences in road-surface characteristics are a known headache for use of global platforms because effective testing is currently only possible after representative prototype vehicles are available, stated Hoyle. “Driver-in-the-loop (DIL) simulators provide a solution by allowing a human driver to experience the vehicle’s behavior in a virtual environment long before physical prototypes exist; however, to be effective they need a level of road-surface detail that has been unavailable until now.”

However, Hoyle says his company has developed a solution: “New surface scanning technology is being utilized by rFpro to produce digital road models with unprecedented accuracy and speed. We can capture and reproduce the differences between a frost-damaged Detroit highway, a smooth German autobahn, a Swiss alpine pass, or any other surface.”

This advance means vehicle manufacturers located anywhere in the world can evaluate their vehicle’s chassis response to any road type in a realistic virtual environment, he claims: “And they can do so without leaving the office.”

The breakthrough in digital road modeling developed by rFpro starts with replacing the usual single pulse laser LIDAR “time-of-flight” scanning process with new scanning technology that uses a number of separate, phased laser signals. Instead of waiting for each signal to return before firing the next one, the controlled phasing allows the signals to be overlapped, increasing the speed, quantity, and quality of data captured.

“The new process provides up to 50 times the level of detail with greater accuracy than ever before; it’s also faster, which allows the scanners to drive at normal road speeds rather than at a crawl,” said Hoyle. “This makes it realistic to scan much longer sections of a chosen route, even during the day, without impeding other road users.”

To provide global coverage, the company works with a core group of regionally located scanning partners, all of whom have now added the latest phase-based scanning capability to the systems they use for rFpro’s road surveys.

Because it reduces development time, the new technology has the potential to make a significant reduction in costs.

Accuracy is such that a road surface can be “sampled” every 5 mm (0.2 in) with a precision down to less than 1 mm (0.04 in).

Growing numbers of vehicle manufacturers are asking for their favorite test routes to be captured and reproduced, according to Hoyle: “For example, in 2012 we built just over 100 km of digital road models; in 2014, it was more than 1000 km, and this year we expect to build approximately 3000 km.”

He added that the increasing volumes of data have led rFpro to switch to cloud-based processing and storage, providing almost limitless scope for further growth and enabling hundreds of CPU cores to work simultaneously on data processing, further reducing timescales: “Traditionally, manufacturers have used simulators adapted from the aerospace industry, but to get the most from these digital road models it is essential to use state-of-the-art, purpose built automotive simulators.”

This year, rFpro has begun working with several major OEMs on advanced driver assistance systems for autonomous vehicles, although Hoyle will not comment on the likelihood of the commercialization of autonomous vehicles by 2020, a timescale for which that some companies are aiming.

Current developments in the automotive industry, such as the growing use of shared platforms and autonomous vehicles, are increasing the importance of virtual testing.

The company’s TerrainServer software is currently being used by OEMs in North America, Asia, and Europe as well as by most of the F1 and NASCAR teams, stated Hoyle. It takes the digital track (or road) surface model together with other inputs including tire contact patches, and processes the information in real time, to achieve accurate vehicle dynamic simulations. Hoyle explained that the real-time aspect of this capability is essential for DIL testing.